Biometric security, e.g., involving finger prints, facial recognition, etc., has become a rapidly expanding method for identifying individuals for verification and identification purposes. In a typical system, a sensor is used to collect biometric data, e.g., via an image acquisition system. After the data is preprocessed (e.g., to remove artifacts), it is passed to a feature extractor. The features are generally captured as a set of feature vectors. The feature vectors are used to create a template. A template is a synthesis of all the characteristics extracted from the source generally in the form of x, y, alpha, and theta, which is used by a biometric matcher.
If training is being performed, the template is simply stored somewhere (e.g., on a card or within a database or both). In a matching phase, the obtained template is passed to a matcher that compares the inputted template with other existing templates, estimating the distance between them using any algorithm (e.g., Hamming distance). A result can then be output for a specified use or purpose (e.g., entrance in a restricted area).
Biometric systems have several areas of vulnerabilities that could jeopardize user privacy and alter the receiver operating characteristic (ROC) curve. One such ROC curve is a plot of True Accept rates versus False Accept rates that provide measurements to compare various biometric systems. At the sensor level, a user could present a fake biometric and attempt to spoof a legitimate biometric. After the signal acquisition (e.g., fingerprint), the image or wavelet scalar quantization file is sent to the feature extraction module. A user could circumvent the signal acquisition stage and submit a previously enrolled image or digitally modified image to the feature extraction module. On the feature extraction module, a Trojan horse piece of malicious code might produce feature vectors that optimize the chances of a false accept.
Additionally, a feature vector set could be tampered with to produce false feature sets. The feature vector vulnerability is critical for clustering algorithms. Furthermore, if the feature extraction and matcher modules are separated, the socket connection (TCP/IP) providing a network interface is vulnerable for template interception. Further, each matcher on the system could be attacked resulting in the production of pre-selected scores. Also, templates or feature vectors that represent biometric feature extraction may be stored within remote or local centralized or distributed databases. The servers that house the databases are vulnerable to attack such that an intruder could steal or modify the templates. The results could increase the false accept and the false negative rates. Moreover, as stored templates are sent to the matcher, the features could be intercepted and modified. After the matching process is complete, the deterministic or probabilistic answer could be overridden by a hacker. Accordingly, biometric systems are subject to numerous vulnerabilities.